Weakly supervised pre-training for brain tumor segmentation using principal axis measurements of tumor burden - Mckone, Joshua E.; Lambrou, Tryphon; Ye, Xujiong; Brown, James M. (Frontiers in Computer Science, 2024)
State-of-the-art multi-modal brain tumour segmentation methods often rely on large quantities of manually annotated data to produce acceptable results. In settings where such labelled data may be scarce, there may be value in exploiting cheaper or more readily available data through clinical trials, such as Response Assessment in Neuro-Oncology (RANO). This work demonstrates the utility of such measurements for multi-modal brain tumour segmentation, whereby an encoder network is first trained to regress synthetic "Pseudo-RANO" measurements, using a mean squared error loss with cosine similarity penalty to promote orthogonality of the principal axes.Using oriented bounding-boxes to measure overlap with the ground truth, we show that the encoder model can reliably estimate tumour principal axes with good performance. The trained encoder was combined with a randomly initialised decoder for fine-tuning as a U-Net architecture for whole tumour (WT) segmentation. Our results demonstrate that weakly supervised encoder models converge faster than those trained without pre-training and help minimise the annotation burden when trained to perform segmentation.